OpenRaiser/Intern-Atlas huggingface dataset:
Improved Answer Selection with Pre-Trained Word Embeddings
Venue: arXiv cs.IR 2017
Year: 2017

This paper evaluates existing and newly proposed answer selection methods based on pre-trained word embeddings. Word embeddings are highly effective in various natural language processing tasks and their integration into traditional information retrieval (IR) systems allows for the capture of semantic relatedness between questions and answers. Empirical results on three publicly available data sets show significant gains over traditional term frequency based approaches in both supervised and unsupervised settings. We show that combining these word embedding features with traditional learning-to-rank techniques can achieve similar performance to state-of-the-art neural networks trained for the answer selection task.
Spectral Methods for Passive Imaging: Non-asymptotic Performance and Robustness
Venue: arXiv cs.IT 2017
Year: 2017

We study the problem of passive imaging through convolutive channels. A scene is illuminated with an unknown, unstructured source, and the measured response is the convolution of this source with multiple channel responses, each of which is time-limited. Spectral methods based on the commutativity of convolution, first proposed and analyzed in the 1990s, provide an elegant mathematical framework for attacking this problem. However, these now classical methods are very sensitive to noise, especially when working from relatively small sample sizes. In this paper, we show that a linear subspace model on the coefficients of the impulse responses of the channels can make this problem well-posed. We derive non-asymptotic error bounds for the generic subspace model by analyzing the spectral gap of the cross-correlation matrix of the channels relative to the perturbation introduced by noise. Numerical results show that this modified spectral method offers significant improvements over the classical method and outperforms other competing methods for multichannel blind deconvolution.
Resource Allocation in Shared Spectrum Access Communications for Operators with Diverse Service Requirements
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study inter-operator spectrum sharing and intra-operator resource allocation in shared spectrum access communication systems and propose efficient dynamic solutions to address both inter-operator and intra-operator resource allocation optimization problems. For inter-operator spectrum sharing, we present two competent approaches, namely the subcarrier gain based sharing and fragmentation based sharing, which carry out fair and flexible allocation of the available shareable spectrum among the operators subject to certain well-defined sharing rules, traffic demands and channel propagation characteristics. Subcarrier gain based spectrum sharing scheme has been found to be more efficient in terms of achieved throughput. However, fragmentation based sharing is more attractive in terms of computational complexity. For intra-operator resource allocation, we consider resource allocation problem with users' dissimilar service requirements, where the operator supports users with delay-constraint and non-delay constraint service requirements, simultaneously. This optimization problem is a mixed integer nonlinear programming problem and nonconvex, which is computationally very expensive, and the complexity grows exponentially with the number of integer variables. We propose less-complex and efficient suboptimal solution based on formulating exact linearization, linear approximation and convexification techniques for the nonlinear and/or non-convex objective functions and constraints. Extensive simulation performance analysis has been carried out that validates the efficiency of the proposed solution.
A class of cyclotomic linear codes and their generalized Hamming weights
Venue: arXiv cs.IT 2017
Year: 2017

Firstly, we give a formula on the generalized Hamming weight of linear codes constructed generically by defining sets. Secondly, by choosing properly the defining set we obtain a class of cyclotomic linear codes and then present two alternative formulas to calculate their generalized Hamming weights. Lastly, we determine their weight distribution and generalized Hamming weights partially. Especially, we solved the generalized Hamming weights completely in one case.
Efficient and Secure Routing Protocol for WSN-A Thesis
Venue: arXiv cs.NI 2017
Year: 2017

Advances in Wireless Sensor Network (WSN) have provided the availability of small and low-cost sensors with the capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. Since WSN protocols are application specific, the focus has been given to the routing protocols that might differ depending on the application and network architecture. In this work, novel routing protocols have been proposed which is a cluster-based security protocol is named as Efficient and Secure Routing Protocol (ESRP) for WSN. The goal of ESRP is to provide an energy efficient routing solution with dynamic security features for clustered WSN. During the network formation, a node which is connected to a Personal Computer (PC) has been selected as a sink node. Once the sensor nodes were deployed, the sink node logically segregates the other nodes in a cluster structure and subsequently creates a WSN. This centralized cluster formation method is used to reduce the node level processing burden and avoid multiple communications. In order to ensure reliable data delivery, various security features have been incorporated in the proposed protocol such as Modified Zero-Knowledge Protocol (MZKP), Promiscuous hearing method, Trapping of adversaries and Mine detection. One of the unique features of this ESRP is that it can dynamically decide about the selection of these security methods, based on the residual energy of nodes.
Privacy-Preserving Mechanisms for Parametric Survival Analysis with Weibull Distribution
Venue: arXiv cs.CR 2017
Year: 2017

Survival analysis studies the statistical properties of the time until an event of interest occurs. It has been commonly used to study the effectiveness of medical treatments or the lifespan of a population. However, survival analysis can potentially leak confidential information of individuals in the dataset. The state-of-the-art techniques apply ad-hoc privacy-preserving mechanisms on publishing results to protect the privacy. These techniques usually publish sanitized and randomized answers which promise to protect the privacy of individuals in the dataset but without providing any formal mechanism on privacy protection. In this paper, we propose private mechanisms for parametric survival analysis with Weibull distribution. We prove that our proposed mechanisms achieve differential privacy, a robust and rigorous definition of privacy-preservation. Our mechanisms exploit the property of local sensitivity to carefully design a utility function which enables us to publish parameters of Weibull distribution with high precision. Our experimental studies show that our mechanisms can publish useful answers and outperform other differentially private techniques on real datasets.
A Review of Internet of Things Architecture, Technologies and Analysis Smartphone-based Attacks Against 3D printers
Venue: arXiv cs.NI 2017
Year: 2017

Human beings cannot be happy with any kind of tiredness based work, so they focused on machines to work on behalf of humans. The Internet-based latest technology provides the platforms for human beings to relax and unburden feeling. The Internet of Things (IoT) field efficiently helps human beings with smart decisions through Machine-to-Machine (M2M) communication all over the world. It has been difficult to ignore the importance of the IoT field with the new development of applications such as a smartphone in the present era. The IoT field sensor plays a vital role in sensing the intelligent object/things and making an intelligent decision after sensing the objects. The rapid development of new applications using smartphones in the world caused all users of the IoT community to be faced with one major challenge of security in the form of side channel attacks against highly intensive 3D printing systems. The smartphone formulated Intellectual property (IP) of side channel attacks investigate against 3D printer in the physical domain through reconstructed G-code file through primitive operations. The smartphone (Nexus 5) solved the main problems such as orientation fixing, model accuracy of frame size and validate the feasibility and effectiveness in real case studies against the 3D printer. The 3D printing estimated value reached 20.2 billion of dollars in 2021. The thermal camera is used for exploring the side channel attacks after reconstructing the objects against 3D printers. The researcher analyzed IoT security relevant issues which were avoided in future by enhanced strong security mechanism strategy, encryption, and machine learning-based algorithms, latest technologies, schemes and protocols utilized in an efficient way. Keywords: - Internet of Things (IoT), Machine-to-Machine (M2M), Security, 3D printer, smartphone
Fast Modeling Methods for Complex System with Separable Features
Venue: arXiv cs.NE 2017
Year: 2017

Data-driven modeling plays an increasingly important role in different areas of engineering. For most of existing methods, such as genetic programming (GP), the convergence speed might be too slow for large scale problems with a large number of variables. Fortunately, in many applications, the target models are separable in some sense. In this paper, we analyze different types of separability of some real-world engineering equations and establish a mathematical model of generalized separable system (GS system). In order to get the structure of the GS system, two concepts, namely block and factor are introduced, and a special method, block and factor detection is also proposed, in which the target model is decomposed into a number of blocks, further into minimal blocks and factors. Compare to the conventional GP, the new method can make large reductions to the search space. The minimal blocks and factors are optimized and assembled with a global optimization search engine, low dimensional simplex evolution (LDSE). An extensive study between the proposed method and a state-of-the-art data-driven fitting tool, Eureqa, has been presented with several man-made problems. Test results indicate that the proposed method is more effective and efficient under all the investigated cases.
Machine Learning for Survival Analysis: A Survey
Venue: arXiv cs.LG 2017
Year: 2017

Accurately predicting the time of occurrence of an event of interest is a critical problem in longitudinal data analysis. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a phenomenon is called censoring which can be effectively handled using survival analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome this censoring issue. In addition, many machine learning algorithms are adapted to effectively handle survival data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in survival analysis and provide a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.
Polylogarithmic approximation for minimum planarization (almost)
Venue: arXiv cs.DS 2017
Year: 2017

In the minimum planarization problem, given some $n$-vertex graph, the goal is to find a set of vertices of minimum cardinality whose removal leaves a planar graph. This is a fundamental problem in topological graph theory. We present a $\log^{O(1)} n$-approximation algorithm for this problem on general graphs with running time $n^{O(\log n/\log\log n)}$. We also obtain a $O(n^\varepsilon)$-approximation with running time $n^{O(1/\varepsilon)}$ for any arbitrarily small constant $\varepsilon > 0$. Prior to our work, no non-trivial algorithm was known for this problem on general graphs, and the best known result even on graphs of bounded degree was a $n^{Ω(1)}$-approximation [Chekuri and Sidiropoulos 2013]. As an immediate corollary, we also obtain improved approximation algorithms for the crossing number problem on graphs of bounded degree. Specifically, we obtain $O(n^{1/2+\varepsilon})$-approximation and $n^{1/2} \log^{O(1)} n$-approximation algorithms in time $n^{O(1/\varepsilon)}$ and $n^{O(\log n/\log\log n)}$ respectively. The previously best-known result was a polynomial-time $n^{9/10}\log^{O(1)} n$-approximation algorithm [Chuzhoy 2011]. Our algorithm introduces several new tools including an efficient grid-minor construction for apex graphs, and a new method for computing irrelevant vertices. Analogues of these tools were previously available only for exact algorithms. Our work gives efficient implementations of these ideas in the setting of approximation algorithms, which could be of independent interest.
Hypotheses generation using link prediction in a bipartite graph
Venue: arXiv cs.IR 2017
Year: 2017

The large volume of scientific publications is likely to have hidden knowledge that can be used for suggesting new research topics. We propose an automatic method that is helpful for generating research hypotheses in the field of physics using the massive number of physics journal publications. We convert the text data of titles and abstract sections in publications to a bipartite graph, extracting words of physical matter composed of chemical elements and extracting related keywords in the paper. The proposed method predicts the formation of new links between matter and keyword nodes based on collaborative filtering and matter popularity. The formation of links represents research hypotheses, as it suggests the new possible relationships between physical matter and keywords for physical properties or phenomena. The suggested method has better performance than existing methods for link prediction in the entire bipartite graph and the subgraph that contains only a specific keyword, such as `antiferromagnetism' or `superconductivity.'
An Improved Neural Segmentation Method Based on U-NET
Venue: arXiv cs.CV 2017
Year: 2017

Neural segmentation has a great impact on the smooth implementation of local anesthesia surgery. At present, the network for the segmentation includes U-NET [1] and SegNet [2]. U-NET network has short training time and less training parameters, but the depth is not deep enough. SegNet network has deeper structure, but it needs longer training time, and more training samples. In this paper, we propose an improved U-NET neural network for the segmentation. This network deepens the original structure through importing residual network. Compared with U-NET and SegNet, the improved U-NET network has fewer training parameters, shorter training time and get a great improvement in segmentation effect. The improved U-NET network structure has a good application scene in neural segmentation.
When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies
Venue: arXiv cs.CR 2017
Year: 2017

We show how third-party web trackers can deanonymize users of cryptocurrencies. We present two distinct but complementary attacks. On most shopping websites, third party trackers receive information about user purchases for purposes of advertising and analytics. We show that, if the user pays using a cryptocurrency, trackers typically possess enough information about the purchase to uniquely identify the transaction on the blockchain, link it to the user's cookie, and further to the user's real identity. Our second attack shows that if the tracker is able to link two purchases of the same user to the blockchain in this manner, it can identify the user's entire cluster of addresses and transactions on the blockchain, even if the user employs blockchain anonymity techniques such as CoinJoin. The attacks are passive and hence can be retroactively applied to past purchases. We discuss several mitigations, but none are perfect.
Design-Time Quantification of Integrity in Cyber-Physical-Systems
Venue: arXiv cs.CR 2017
Year: 2017

In a software system it is possible to quantify the amount of information that is leaked or corrupted by analysing the flows of information present in the source code. In a cyber-physical system, information flows are not only present at the digital level, but also at a physical level, and to and fro the two levels. In this work, we provide a methodology to formally analyse a Cyber-Physical System composite model (combining physics and control) using an information flow-theoretic approach. We use this approach to quantify the level of vulnerability of a system with respect to attackers with different capabilities. We illustrate our approach by means of a water distribution case study.
Efficient Compression Technique for Sparse Sets
Venue: arXiv cs.IT 2017
Year: 2017

Recent technological advancements have led to the generation of huge amounts of data over the web, such as text, image, audio and video. Most of this data is high dimensional and sparse, for e.g., the bag-of-words representation used for representing text. Often, an efficient search for similar data points needs to be performed in many applications like clustering, nearest neighbour search, ranking and indexing. Even though there have been significant increases in computational power, a simple brute-force similarity-search on such datasets is inefficient and at times impossible. Thus, it is desirable to get a compressed representation which preserves the similarity between data points. In this work, we consider the data points as sets and use Jaccard similarity as the similarity measure. Compression techniques are generally evaluated on the following parameters --1) Randomness required for compression, 2) Time required for compression, 3) Dimension of the data after compression, and 4) Space required to store the compressed data. Ideally, the compressed representation of the data should be such, that the similarity between each pair of data points is preserved, while keeping the time and the randomness required for compression as low as possible. We show that the compression technique suggested by Pratap and Kulkarni also works well for Jaccard similarity. We present a theoretical proof of the same and complement it with rigorous experimentations on synthetic as well as real-world datasets. We also compare our results with the state-of-the-art "min-wise independent permutation", and show that our compression algorithm achieves almost equal accuracy while significantly reducing the compression time and the randomness.
A Generalised Directional Laplacian Distribution: Estimation, Mixture Models and Audio Source Separation
Venue: arXiv cs.SD 2017
Year: 2017

Directional or Circular statistics are pertaining to the analysis and interpretation of directions or rotations. In this work, a novel probability distribution is proposed to model multidimensional sparse directional data. The Generalised Directional Laplacian Distribution (DLD) is a hybrid between the Laplacian distribution and the von Mises-Fisher distribution. The distribution's parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data. The author explores the application of the derived DLD mixture model to cluster sound sources that exist in an underdetermined instantaneous sound mixture. The proposed model can solve the general K x L (K<L) underdetermined instantaneous source separation problem, offering a fast and stable solution.
Maximum A Posteriori Inference in Sum-Product Networks
Venue: arXiv cs.AI 2017
Year: 2017

Sum-product networks (SPNs) are a class of probabilistic graphical models that allow tractable marginal inference. However, the maximum a posteriori (MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from both theoretical and algorithmic perspectives. For the theoretical part, we reduce general MAP inference to its special case without evidence and hidden variables; we also show that it is NP-hard to approximate the MAP problem to $2^{n^ε}$ for fixed $0 \leq ε< 1$, where $n$ is the input size. For the algorithmic part, we first present an exact MAP solver that runs reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in our experiments. We then present a new approximate MAP solver with a good balance between speed and accuracy, and our comprehensive experiments on real-world datasets show that it has better overall performance than existing approximate solvers.
Top-Down Synthesis of Multi-Agent Formation Control: An Eigenstructure Assignment based Approach
Venue: arXiv cs.SY 2017
Year: 2017

We propose a top-down approach for formation control of heterogeneous multi-agent systems, based on the method of eigenstructure assignment. Given the problem of achieving scalable formations on the plane, our approach globally computes a state feedback control that assigns desired closed-loop eigenvalues/eigenvectors. We characterize the relation between the eigenvalues/eigenvectors and the resulting inter-agent communication topology, and design special (sparse) topologies such that the synthesized control may be implemented locally by the individual agents. Moreover, we present a hierarchical synthesis procedure that significantly improves computational efficiency. Finally, we extend the proposed approach to achieve rigid formation and circular motion, and illustrate these results by simulation examples.
New Approximations for Coalitional Manipulation in General Scoring Rules
Venue: arXiv cs.DS 2017
Year: 2017

We study the problem of coalitional manipulation---where $k$ manipulators try to manipulate an election on $m$ candidates---under general scoring rules, with a focus on the Borda protocol. We do so both in the weighted and unweighted settings. We focus on minimizing the maximum score obtainable by a non-preferred candidate. In the strongest, most general setting, we provide an algorithm for any scoring rule as described by a vector $\vecα=(α_1,\ldots,α_m)$: for some $β=O(\sqrt{m\log m})$, it obtains an additive approximation equal to $W\cdot \max_i \lvert α_{i+β}-α_i \rvert$, where $W$ is the sum of voter weights. For Borda, both the weighted and unweighted variants are known to be $NP$-hard. For the unweighted case, our simpler algorithm provides a randomized, additive $O(k \sqrt{m \log m} )$ approximation; in other words, if there exists a strategy enabling the preferred candidate to win by an $Ω(k \sqrt{m \log m} )$ margin, our method, with high probability, will find a strategy enabling her to win (albeit with a possibly smaller margin). It thus provides a somewhat stronger guarantee compared to the previous methods, which implicitly implied a strategy that provides an $Ω(m)$-additive approximation to the maximum score of a non-preferred candidate. For the weighted case, our generalized algorithm provides an $O(W \sqrt{m \log m} )$-additive approximation, where $W$ is the sum of voter weights. This is a clear advantage over previous methods: some of them do not generalize to the weighted case, while others---which approximate the number of manipulators---pose restrictions on the weights of extra manipulators added. Our methods are based on carefully rounding an exponentially-large configuration linear program that is solved by using the ellipsoid method with an efficient separation oracle.